The Influence of Business Group Factors on the Financial

International Journal of Scientific and Research Publications, Volume 11, Issue 6, June 2021 255

ISSN 2250-3153

This publication is licensed under Creative Commons Attribution CC BY.

http://dx.doi.org/10.29322/IJSRP.11.06.2021.p11435 www.ijsrp.org

The Influence of Business Group Factors on the

Financial Performance of Informal Micro Retail

Enterprises in Nairobi.

Caroline Grace Wanjiku *, Patricia Chepwogen Chepkwony **

DOI: 10.29322/IJSRP.11.06.2021.p11435

http://dx.doi.org/10.29322/IJSRP.11.06.2021.p11435

Abstract- The study focused on the informal micro retail sector in

Nairobi researching on the enterprises in the Smart Duka project

run by a Non-Governmental Organization and investigating the

use of group participation and its effect on the financial

performance of these enterprises which are located in the informal

settlements of Nairobi. Prior studies have focused more on areas

outside the informal sectors of Nairobi and have had limited focus

on the effect of group participation on financial performance of

enterprises which the current study sought to address. The

financial performance and business factors of the enterprises that

joined groups (treated group) were compared to financial

performance and business factors of the enterprises that did not

join groups (control group) in the project. The research design

used a triangulation approach using an experimental and

explanatory designs to study causal links investigating if there is

a link between variables. Primary and secondary data from the

project was used to get quantitative data collected from the sample

of 116 retail enterprises in groups randomly picked using

probability sampling from the project and 116 retail enterprises

not in groups. Based on the results of the study, the null hypothesis

of financial performance of enterprises in business groups being

less or equal to that of enterprises not in business groups was

accepted due to the difference between the treated and control

groups being statistically insignificant despite being higher and

hence ways of making this difference significant should be

addressed. The managerial factors had positive insignificant

effects, strategic fit factors had positive significant effects while

financial factors had negative insignificant effects on financial

performance of the enterprises. The study offered novelty by

focusing on the most marginalized of micro enterprises– those

operating in informal settlements within Kenya’s capital – Nairobi

while considering the effect of group networks and magnitude

effect of group factors on financial performance of micro

enterprises.

Index Terms- Informal sector, Group factors, Micro-enterprises,

Financial performance

I. INTRODUCTION

ackground of the Study In Kenya, unemployment has been one of the major issues

affecting the nation and stood at 7.4% in 2015/2016 while the

global unemployment rate was at 5.7% in 2017 and 5.3% in 2018

(Economic Survey, 2018) and (Economic Survey, 2019).

Economic Survey (2019) reported that the economy created

840,600 new jobs in 2018 of which 83.6% were in the informal

sector. As per National Micro, Small and Medium Establishment

Survey by KNBS (2016) the micro enterprises under the category

of wholesale & retail trade enterprises cover a large proportion of

the informal Micro Small and Medium Enterprises, hence there is

need to investigate on ways in which these enterprises can increase

their financial performance for their sustainable growth to provide

more employment opportunities in Kenya.

The National Micro Small and Medium Enterprises Survey

by KNBS (User, 2016) categorizes Micro Small and Medium

Enterprises using number of employees ; Micro are those that have

1-9 employees, Small have 10-49 employees and Medium 50-99

employees. As Chatterjee and Wehrhahn (2017) posit, most

economies depend on the financial contributions of Small and

Medium Enterprises with up to 95% of global enterprises being

Small and Medium Enterprises with the proportion being as high

as 99% in countries such as Japan. A World Bank report further

highlights that up to 60% of total employment and 40% of national

GPD in emerging countries is contributed by Small and Medium

Enterprises (The World Bank, 2019). These support their

significance in various economies while portraying the

significance of the study.

Beck and Cull (2014), focusing on the situation in Africa

posit that despite the high reliance of economies on small and

micro-enterprises, the financial situation in the continent is by and

large characterized by financial systems that are small, costly and

shallow; these institutions are therefore often unable to supply

much needed support for micro enterprises in the region. The

resulting situation in the continent, as Brink Asah et al. (2015)

observe, is the failure of up to 80% of entrepreneurial ventures. It

may therefore be inferred that micro enterprises play a vital role in

global economies and particularly so in Africa. However, such

establishments in the continent face eminent ruin on account of a

stark lack of support.

The informal sector in Kenya has been growing rapidly and

is currently the highest form of employment. According to the

ATKearney Global Consumer Institute (2016), the informal sector

in Kenya accounts for USD 26 billion with the sector contributing

to 35% of the country’s GDP. As per the National Micro, Small

and Medium Establishment Survey by Kenya Bureau of Statistics

(2016), many Micro, Small and Medium Enterprises are

undocumented enterprises and are informal while most of the

Micro, Small and Medium Enterprises are micro enterprises and

are operated by owners with a few or no employees. The bigger

firms which are few, are owned by individuals who are highly

B


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International Journal of Scientific and Research Publications, Volume 11, Issue 6, June 2021 256 ISSN 2250-3153



educated. As per the National Micro, Small and Medium

Establishment Survey by Kenya Bureau of Statistics (2016) the

Micro sector makes major contributions to the economic and

social sectors of the country by large scale employment across the

country.

Many of the business owners adopt self-sponsored training

indicating that business owners are aware of their deficiency in

skills and seek training to improve their entrepreneurial skills

(Bureau of Statistics, 2016). The 2016 survey further indicates that

the highest concentration of Micro Small Medium Enterprises is

in micro categories as majority of such enterprises are operated by

their owners. It is therefore apparent that there is potential for

invigorating the micro subsector through offering adequate

support for such establishments.

Kamunge, Njeru & Tirimba (2014), in their study on factors

affecting the performance of micro and small enterprises located

in Limuru, Kenya included such factors as; access to finance,

management experience, business information access,

infrastructure access, government policy and regulations. These

results were congruent with the study results by Ntakobajira.

Ntakobajira (n.d.) posits in the study in 2013 on SMEs in City Park

Nairobi that, access to finance, business information and

experience of managers need to be all made available for SMEs to

realize their full potential and also recommend on having

infrastructures in place to share business information with large

numbers of entrepreneurs.

Maina (n.d.) in the study of 2016 on factors affecting growth

of micro enterprises and small family enterprises in Nairobi

central business district recommends that managers and

employees of these organizations should be given training to

enhance performance skills. This recommendation supports the

conclusion of the study by Siekei, Wagoki and Kalio who cited

financial training as an important aspect in performance and

growth of SMEs. Siekei, Wagoki and Kalio (2013) in their study

on funding initiatives by equity bank Kenya and on the financial

training aspects provided to select SMEs in Njoro, Kenya, their

findings indicate that businesses that participated in the

bookkeeping training fared better than those that did not.

Mungai & Ogot (2017) in their study in Muranga, Kenya

conclude that micro enterprises use diverse competitive business

strategies and pointed out the ones that dominate are; the value

chain approaches and horizontal networks and linkages, which

improve performance. Micro enterprise participation in value

chains involve vertical linkages both forward and backward with

larger enterprises while horizontal linkages are informal and

formal networks with enterprises of similar size either directly or

through associations and organizations. However, they do mention

that these alternative strategies were not covered in their study and

were an opportunity of further research.

This study centres on the provision of support through

business group participation by micro enterprises operating in

informal settlements in Nairobi. The study focused on the informal

micro retail sector in Nairobi researching on enterprises in the

Smart Duka project run by a Non-Governmental Organization and

investigated the use of business group participation factors and

their effect on the financial performance of these enterprises. In

the project, various formal practices are used, which include

training, merchandizing, record keeping, customer service, use of

technology, supplier management, loan accessibility and group

associations. The groups formed have been used to access various

facilities in the market including supplier group discounts, ease of

delivery of merchandize to the enterprises, accessibility to loans

and better negotiating power. The three main aspects and factors

of business group participation include – financial, managerial,

and strategic-fit interventions. This study therefore sought to fill

the gap in research by providing empirical evidence of the impact

of group participation in the financial performance on micro

enterprises while considering various factors under the financial,

managerial and strategic fit categories. The study further offered

novelty by focusing on the most marginalized of micro

enterprises– those operating in informal settlements within

Kenya’s capital – Nairobi while considering the effect of group

networks and magnitude effect of group factors on financial

performance of micro enterprises. Prior studies have put their

focus more on areas outside the informal sectors of Nairobi and

limited focus on the effect of group participation has been

accorded in prior studies which the current study seeks to address.

1.2 Statement of the Problem Previous literature has examined factors such as access to

finance, business information access, infrastructure, training,

business linkages and government policy as variables that

would influence financial performance of micro-retail

enterprises however these do not offer alternative strategies

that are geared towards financial performance of the micro-

retail enterprises. Scant literature examines the relationship

between business group participation factors and financial

performance while considering various factors under the

financial, managerial and strategic fit categories which have

been highlighted in various studies as important factors in the

financial performance of enterprises. The study offers

novelty by focusing on the most marginalized of micro

enterprises– those operating in informal settlements within

Kenya’s capital – Nairobi while considering the effect of

group networks and effect of group factors on financial

performance of micro enterprises.

1.3 Research Objectives

1.3.1 General Objective The general objective of the study is to establish the

relationship between business group factors deemed effectful on

micro enterprise financial performance measured by sales growth.

The specific objectives are highlighted below.

1.3.2 Specific Objectives 1. To assess the effect of financial factors in business groups

on the financial performance of informal micro retail

enterprises in the Smart Duka project.

2. To assess the effect of managerial factors in business

groups on the financial performance of informal micro

retail enterprises in the Smart Duka project.

3. To assess the effect of group strategic-fit factors in business


retail enterprises in Smart Duka project.

1.3.3 Research Questions 1. What is the effect of financial factors in business groups

on the financial performance of informal micro retail

enterprises in Smart Duka project?


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2. What is the effect of managerial factors in business


retail enterprises in Smart Duka project?

3. What is the effect of strategic fit factors in business


retail enterprises in Smart Duka project?

II. LITERATURE REVIEW

Financial factors and financial performance of

Informal Micro Enterprises Tinajero & Lopez-Acevedo (2010) found in their study of

SME programs in Colombia, Peru, Mexico and Chile, that

participation in SME programs results in increased sales across the

various countries. The study was motivated by the fact that SMEs

make up a high proportion of enterprises and workforce but

perform poorly in comparison to larger firms due to the constraints

they face; lack of finance, management issues, lack of skilled

workers, in economies of scale, poor technology, lack of

information and markets, bureaucratic processes of start-up,

operations and growth. Programs are put in place for SMEs for

skills development, training on management, upgrade of

technology, improve productivity and quality and for network

formation. This finding was similar to Kamunge, Njeru & Tirimba

(2014) in their study in Muranga who cited business information

and infrastructure access as important factors in the financial

performance of micro enterprises.

Kodongo & Kendi (2013) from their study results conclude

that among micro finance institution clients, a group lending

program is more effective than individual lending program to

reduce the risk of default. This implies a collaborative lending

approach centring on the grouping of micro enterprises to allow

for financial competencies through extension of loans to groups.

This approach of leveraging groups to allow for allocation of

resources is in keeping with the RVB and SET theory. However,

they do also conclude in their study that Kenyan microfinance

institutions prefer individual lending which does not meet the

borrowers’ financials needs and is wasteful (Kodongo & Kendi,

2013).

Beck & Cull (2014) state that more than 95% of enterprises

are under micro, small or medium sized categories while more

than half the population of employees work in companies with less

than 100 employees in the low and lower middle income countries

as per finding by (Ayyagari, Demirguc-Kunt and Maksimovic

2011). Beck & Cull (2014), posit these enterprises experience

operational and growth obstacles such as lack of access to

financial services most notably, lending services as is also

highlighted by (Tinajero & Lopez-Acevedo, 2010). For these

smaller enterprises, the following are proposed; leasing is

preferable due to lack of collateral requirements as the option

provides the asset instead of financial resources, factoring (the

discounting of sales receivables), financial innovations,

segmenting the enterprises as per profiles, financial needs and

equity finance.

Beck & Cull (2014) also posit that growth of firms is due to

individuals who have different levels of management skills,

education and motivation and hence understanding the effect of

social networks and education is important for success in

entrepreneurship, which was also also supported by Kamunge,

Njeru & Tirimba (2014) who highlighted management experience

as an important factor for financial performance. The authors,

through a regression model, point to similarity in financing

determinants across regions in that such factors as age of

enterprise, sector and firm size play a pivotal role in determining

the financing approaches applied by companies. However, the

authors posit that the financial situation in Africa is characterized

by financial systems that are small, shallow and costly and often

limited in reach hence highlighting the need for innovation in

financing approaches and particularly so for micro enterprises.

According to statistics presented in contrasting financing by

company size, it was apparent that companies with less than 20

employees (micro enterprises) were significantly underfunded

across all loan categorizations. This finding in light of the RBV

theory would therefore imply that a collaborative lending

approach centring on the grouping of micro enterprises can be

leveraged to allow for financial competencies through extension

of loans to groups with less than 20 employees which is supported

by SET theory. Such an innovative approach would navigate the

highlighted challenge of inaccessible financing for the segment.

The recommendation by Beck & Cull (2014) on innovation in

financing approaches was in line with the recommendation by

Chatterjee & Wehrhahn (2017) to create innovative products that

leverage on pooling of resources.

Kadocsa & Francsovics (2011) in their study of SMEs in

Hungary conclude that after Hungary’s accession to the European

Union, the small businesses did not take advantage of the

European Union new markets and opportunities due to lack of

information. The eventual situation therefore, was that

organizations were operating in a promising market yet did not

leverage the tools that were at their disposal. The authors highlight

networking among enterprises as a possible approach towards

remedying the situation in the country. The study therefore

indicates that economic competencies can be developed through

partnership aimed at identifying opportunities in the broader

market. Mungai & Ogot (2017) in their study in Muranga also did

highlight networks and linkages as important factors for financial

performance.

Kadocsa & Francsovics (2011) in their study concluded that

two thirds of SMEs consider the economy as stagnating or slightly

improving and mainly sell in the retail market and have moderate

development and mostly use their own resources. The study

further found that a reliable monetary policy had the most impact

on business operation among economic policy factors while for

the micro economic factors the important ones were cost

management, marketing, trade, financing, technical development

and production. Chatterjee and Wehrhahn (2015) provide telling

statistics on the prominence of SMEs to the global economy - up

to 95% of enterprises are SMEs; these account for up to 90% of

private sector employment. The proportion of SMEs differs by

country with countries such as Japan posting up to 99% of SME

representation. Further highlighting the prominence of the micro-

SME sector, the authors point to a World Bank study indicating

that up to 70% of the MSMEs are operating in the informal sector

and are mainly defined as micro enterprises. However, despite the

high proportion in the global economy such enterprises remain

heavily underfunded.


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Creation of micro insurer solutions may be the most effective

way of addressing risk among small enterprises in Africa

(Sampson Ukpong, Acha, & Sampson 2019). In a study conducted

in Nigeria, the authors surmise that the challenge of inaccessibility

of funds among small enterprises results in the inability of firms

to meet the threshold requirements for mainstream insurance.

Such enterprises are therefore, in light of lack of options, left

uninsured. To navigate this challenge, the author after assessing 6

possible possibilities, conclude that the formation of cooperatives

would serve to create the common pool of resources that can allow

for the insurance of groups or firms. The authors further observed

that seeking insurance through cooperatives as opposed to

community-based schemes offered the advantage of formality;

where as in the former cooperatives interact with mainstream

insurance companies, community-based schemes were less

formally oriented and therefore, did not require engagement with

mainstream insurance companies that typically serve a regulatory

function. Relating this finding with the previous study by

Chatterjee & Wehrhahn (2017), it is evident that financial

institutions that appreciate the predominance of small enterprises

in the economy of developing countries can create innovative

solutions that allow for the advancement of the much needed

assistance while sufficiently shielding the funders from

defaulters.

Siekei, Wagoki and Kalio (2013) provide a case study of the

performance of 82 farms in Njoro in Kenya. The study centres on

funding initiatives by equity bank Kenya. Specifically, the authors

focus on the financial training aspects provided to select SMEs.

The study seeks to assess whether such competences as

bookkeeping present a significant impact on the financial

performance of firms. Findings indicate that indeed businesses

that opted to participate in the training that was offered to the

group, fared better than those that did not gain the training

provided by the bank. This finding points to the training aspect

involved in group initiatives aimed at achieving financial

performance among small and medium sized enterprises. It is

therefore evident that among the benefits of group participation is

the ease of training, training that has been shown to have a

financial impact on the performance of involved firms. This study

was congruent to the one by Maina (n.d.) regarding training having

a significant impact on financial performance.






conclusion of the study by Siekei, Wagoki and Kalio (2013) who

cited financial training as an important aspect in performance and

growth of SMEs.

Managerial factors and financial performance of

Informal Micro enterprises Whereas financial factors present as the most telling limiting

issue in the performance of small firms, managerial factors also

play a significant role, most SMEs do not have management

solutions that are cost effective and hence cannot compete with

larger firms (Abor & Quartey, 2010). Jeppesen (2005) as the

author observed, weak financial institutions and wanting

regulations, as applicable in different industries, result in peculiar

challenges among small enterprises operating in the continent.

Managerial competencies therefore present as fundamental

determinants of the financial progress of firms. This is similar to

the study of Ntakobajira (n.d.) and Kamunge, Njeru & Tirimba

(2014) who concluded that experience of managers is an important

factor affecting the performance of enterprises.

Atristain-su (2018) in a study of managerial practices in

small and medium enterprises in Mexico, highlights that the

institutions are plagued by a variety of managerial issues. Among

the most noteworthy shortcomings are in the planning, decision

making, delegation, and communication aspects of the companies.

The author points to family business ownership as among the main

reasons behind the lack of professionalism within most of the

institutions considered in the study. The author further points to

the reliance on intuition in management of firms as a possible

reason behind the lack of internal controls within considered

institutions. Essentially, interactions between family members in

their business setting makes creation of controls and measures in

a professional environment a daunting task. The author further

recommends that managers of the various institutions focus on

aligning strategic goals with managerial practices so as to achieve

intended financial outcomes. This is similar with the study by

Asah & Olufunso (2015) who highlight planning as part of the

management skills that increase performance of enterprises.



in Limuru, Kenya included factors such as; infrastructure access,

government policy and regulations.

Solly (2016) in an assessment of the various reasons behind

the failure of small enterprises in South Africa, highlights that

managerial factors play a significant role in determining the

success or lack thereof, of businesses. Employing a qualitative

approach involving the use of interviews, the author posits that

such factors as lack of understanding of approaches to managing

resources present as a main hindrance to entrepreneurs seeking to

set up shop without prior experience. The author proposes a

networking approach whereby seasoned entrepreneurs can use

their experience to equip new business owners with the requisite

skills to ensure efficient management of resources. The author

further proposes consultations with managerial experts who would

highlight specific areas of failures thereby allowing the affected to

better navigate the perils of setting up new businesses. This

approach, as was the case with the previous, emphasises the

validity of the SET as such interventions centre on the idea of

reciprocity in the exchange of valuable information. The

recommendation by Solly (2016) on use of experienced

entrepreneurs is similar to the finding of the study by Ntakobajira

(n.d.) and Kamunge, Njeru & Tirimba (2014) who highlight that

managerial experience is an important factor of enterprise

performance.

Asah, Olufunso, et al.(2015) in an evaluation of the

significance of managerial skills in assessing the performance of

firms conclude that managerial factors, alongside personal value

and motivation, presented as antecedents of favourable economic

performance. The author conceptualized managerial skills as

involving such aspects as financial management skills, strategic

planning, and human resource management concluding that

managers who showed a high competence in such aspects were


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likely to steer their organizations towards improved financial

performance. The inference from this finding is that micro

enterprise managers involved in the exchange of managerial

insights would gain in entrepreneurial management competencies

thus resulting in the gaining of competitive advantage in the

market within which they operate. In line with this is imperative

to study the effect that managerial factors have on micro

enterprises to enable improve their performance. This study is in

line with Atristain-su (2018) who highlights lack of planning as

part of the managerial issues experienced by enterprises in

Mexico.



in Limuru, Kenya included; management experience, business

information access and infrastructure access. These results were

congruent with the study results by Ntakobajira (n.d.). Ntakobajira

(n.d.) posits in the study in 2013 on SMEs in City Park Nairobi

that, business information and experience of managers need to be

all made available for SMEs to realize their full potential and also

recommend on having infrastructures in place to share business

information with large numbers of entrepreneurs.

Strategic-fit factors and financial performance of

Informal Micro enterprises The essence of strategic fit factors as applicable to micro

enterprises is the notion that identifying points of synergy between

institutions operating in the same market environment would

allow for the gaining of competitive advantage. This premise

stems from the RBV theory. Kibry and Kaiser (2003) postulate

that joint ventures, among other competencies, allow for the

common sourcing of raw materials by SMEs. Through this

approach, such small enterprises are able to leverage the

economies of scale to allow for lower payments and in most cases

efficiency gains in the process of sourcing for raw materials. This

reduction in costs and increasing efficiency stems from the fact

that suppliers are better able to deliver products and services in

bulk as opposed to individuals as they cut costs through, for

instance, decreased travel payments when supplying bulky goods.

The practical application of this strategic fit factor in the local

context would involve communal ordering of products and

services from wholesalers to retail establishments involved in a

group partnership. This finding by Kibry and Kaiser (2003) is

similar to the proposal by Mungai & Ogot (2017) on use of vertical

and horizontal linkages or networks and value chains to improve

performance of enterprises.

Hoffmann & Schlosser (2001) in a study involving 164

Australian companies operating in the SME space observed that

strategic alliances are gaining increasing popularity. This is

because companies are able to access competencies that would

otherwise not be available to them except for partnership with

other like-minded firms that offer abilities that are not inherently

available in their respective companies. In assessing the various

determinants of strategic alliances, the authors point out that

factors such as trust predicted the success or lack thereof of

partnership endeavours. However, other more technical factors

such as the nature of the fit between the enterprises play a

significant role in determining the success of partnerships. The

authors further posit that establishing the rights and duties of each

of the parties involved in partnerships and the outlining of the

processes in terms of engagement allow for the streamlining of the

interaction between enterprises seeking to achieve the common

goal of profit making. This study therefore highlights the nuance

involved in achieving effective partnership between firms and

supports the tenets of the SET theory as put up by Cropanzano &

Mitchell (2005) of interactions and obligations which are under

the rules of exchange.

Pindado & Sánchez (2018) opine that the resources

capabilities in the context of the market with which organisations

operate have a significant effect on the growth curve of new

enterprises. Firms that have substantial internal resources and

operate in markets characterised by low competition are likely to

achieve faster growth than their counterparts with limited

resources. This finding therefore indicates that limitation in

resources predicates the need for the seeking out of strategic fits

as companies with few resources strive to find a competitive

advantage. The authors further highlight the pivotal role of

competitive forces within markets; as the level of competition

increases the importance of vast internal resources also increases

with companies with few resources being at the forefront in

experiencing the strains of highly competitive industries. It

therefore may be inferred that companies should seek good

strategic fit options if they anticipate harsh financial conditions

and especially so if operating in highly competitive environments.

The level of competition in the developing countries would be

considered high given the large population of enterprises

especially in the micro retail category as supported by the MSME

survey (2016) done by the KNBS, hence use of strategic

partnerships would assist the micro enterprises leverage from the

advantages the partnerships would offer.

Mashal (2018), in a study focused on non-financial factors

affecting small and medium enterprises in Jordan, observes that

different businesses faced similar challenges. Among the notable

of these challenges, a factor highlighted in the foregoing study, is

the impact of competition. Another noteworthy observation was

that the government policies in place played a significant role in

determining the profitability of enterprises. The inference from

this study therefore is that where such factors as competition

present as challenges to collaboration, the mutual challenges

facing industries should serve to spur strategic partnerships in their

bid to ensure survival. Efforts towards mutual training and

innovation play the role of improving the financial position of

involved entities. This is supported by the study by Pindado &

Sánchez (2018) who highlighted that firms operating in markets

characterized by low competition achieve faster growth while

those with limited resources seek strategic fits to get a competitive

advantage.

Mungai and Ogot (2017) in their study in Muranga conclude

that micro enterprises use diverse competitive business strategies

and pointed out the ones that dominate are; the value chain

approaches and horizontal networks and linkages, which improve

performance. Micro enterprise participation in value chains

involve vertical linkages both forward and backward with larger

enterprises while horizontal linkages are informal and formal

networks with enterprises of similar size either directly or through

associations and organizations. However, they do mention that

these alternative strategies that had shown support towards

performance improvement were not covered under the activity


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based competitive business strategy typologies by Porter and in

their study and were an opportunity of further research. This study

by Mungai and Ogot (2017) does support various aforementioned

studies under this section of strategic fit factors that highlight the

various advantages of synergies and networking such as those by

Kibry and Kaiser (2003) and Pindado & Sánchez (2018).

Financial performance of Informal Micro enterprises Financial performance of the informal micro enterprises

which was the independent variable was measured by sales

growth, profit growth, inventory growth and growth in cash float.

The use of these measures as the dependent variable was

considered as appropriate due to the information available to the

micro enterprises. Bookkeeping is a challenge to most of the

enterprises due to the high granularity product levels they use

hence a relative measure of growth was the form of information

that was available from the micro enterprises rather than absolute

sales figures over the years which were not available. Questions in

relation to the dependent variable to supplement this that were

included were on; sales growth, inventory growth, profit growth

and cash float growth.

Isaksson & Seifert (2014) conclude that managing of

inventories gives businesses competitive advantages which results

in better financial performance. This was in line with the study by

Caroline (n.d.) in 2017 who concluded that in cases where there is

a unit of inventory turnover growth this would lead to increase in

turnover of inventory and that inventory turnover is one of the

variables that affect financial performance. Hence use of inventory

growth as a subfactor of financial performance was used in this

study.

Research Gaps Atristain-su (2018) suggested to have further studies on the

effect of managerial practises on internal controls within family

businesses to determine best performance strategies and for

continuity. Thus, this study accesses the impact of managerial

factors on financial performance of micro enterprises. Kirby &

Kaiser (2003) stated that there were many proponents of the

strategy of joint ventures to enable SMEs with limited resources

and knowledge to enter international markets yet few have focused

on joint venturing activities of SMEs. In this study, partnerships

as a means of joint ventures and group associations were assessed

in terms of their impact on performance on micro enterprises.



in Limuru, Kenya included such factors as; access to finance,

management experience, business information access,

infrastructure access, government policy and regulations. These

results were congruent with the study results by Ntakobajira (n.d.).

Ntakobajira (n.d.) posits in the study in 2013 on SMEs in City Park

Nairobi that, access to finance, business information and

experience of managers need to be all made available for SMEs to

realize their full potential and also recommend on having

infrastructures in place to share business information with large

numbers of entrepreneurs. These studies focussed on micro and

small enterprises in Limuru and small and medium enterprises in

City Park hence there is need to study the micro enterprises

especially in the informal settlements of Nairobi and establish the

effect of these factors on the financial performance of these

enterprises.






conclusion of the study by Siekei, Wagoki and Kalio (2013) who

cited financial training as an important aspect in performance and

growth of SMEs. Siekei, Wagoki and Kalio (2013) in their study

in Njoro on funding initiatives by equity bank Kenya and on the

financial training aspects provided to select SMEs. Their findings

indicate that businesses that participated in the bookkeeping

training fared better than those that did not. These studies were

focussed on the Nairobi central business district and Njoro areas

hence there is need for the study of micro enterprises in the

informal settlements of Nairobi and how such factors affect the

performance of these enterprises which this study focuses on.

Mungai & Ogot (2017) in their study in Muranga conclude

that micro enterprises use diverse competitive business strategies

and pointed out the ones that dominate are; the value chain

approaches and horizontal networks and linkages, which improve

performance. Micro enterprise participation in value chains

involve vertical linkages both forward and backward with larger

enterprises while horizontal linkages are informal and formal

networks with enterprises of similar size either directly or through

associations and organizations. However, they do mention that

these alternative strategies were not covered in their study and

were an opportunity of further research, which this study delves

into.

This study therefore seeks to fill the gap in research by

providing empirical evidence of the effect of group participation

in the financial performance on micro enterprises while

considering various factors under the financial, managerial and

strategic fit categories. The study further offers novelty by

focusing on the most marginalized of micro enterprises– those

operating in informal settlements within Kenya’s capital – Nairobi

while considering the effect of group networks and significance

effect of factors in business groups on financial performance of

micro enterprises.

Conceptual Framework In the study the dependent variable is the financial

performance of the informal micro enterprises, which is measured

by sales growth. The construct is deemed the dependent variable

as the main aim of group interventions as to optimize the

performance of participating firms. The independent variables are

the financial, managerial and strategic fit factors in business

groups; these entail the main means through which business

interventions, through group participation, were effected.


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Figure 1: Conceptual framework

III. METHODOLOGY

3.1 Research Design The research design used a triangulation approach using an

experimental and explanatory designs to study causal links

investigating if there is a link between variables. The study is

based on an explanatory approach, which Saunders et al. (2009)

describe as a study that establishes relationships between

variables, it studies a situation or problem in order to explain the

relationships between variables. The study was based on an

experimental design having a treated group (enterprises in

business groups) and a control group (enterprises not in business

groups) and also had an explanatory purpose to explain the causal

relationship between business group factors (financial, managerial

and strategic-fit factors) and financial performance of the

enterprises.

3.2 Population and Sampling The study focused on a project run by a Non-Governmental

Organization that dealt with informal micro retail enterprises in

the informal sectors of Nairobi. The enterprises in the project were

in informal settlements of Nairobi including Githurai, Kayole,

Mwiki, Kangemi, Kawangware, Huruma, Mathare, Umoja,

Pipeline and Dandora. The main aim of the project was to assist

grow and equip the informal retail micro enterprises.

The population of the informal micro retail enterprises in the

project was 874 from the informal micro enterprises that were

onboarded to the project in various cohorts with the initial baseline

selection being Dec 2015 and the endline in June 2018 (Smart

Duka,2019). The sampling process was quantitative and was done

using probability sampling and using simple random sampling

from the different areas where the project was being carried out in

the informal settlements in Nairobi, Kenya. The sample was

picked putting into consideration time and cost constraints. The

sampling unit was the micro enterprise while the sample element

was the owner of the enterprises or the main manager of the

enterprises. Owners and managers of micro enterprises not in the

Smart Duka Project were not eligible to take part in this study.

The basis of the sample size determination was the ‘standard

formula’:

n= 2 (Z 𝛼 + Z 1-β) 2 σ2/ (Δ2 )

where 𝑛 was the appropriate sample size,

𝑍𝛼 was a constant set according to the accepted 𝛼 error: 𝛼 = 10%

𝑍𝛼 = 1.28; 𝑍1−𝛽 dependent on the desired statistical power of the

test: the study used 80% power, conventionally deemed

reasonable, yielding 𝑍1−𝛽 = 0.8416 Kadam & Bhalerao (2010) ,

∆ is the expected difference in the effect of the two groups (treated

and control group) – approximately 0.07; and 𝜎, the standard

deviation. variability of the population is approximately 0.25, both

from the (Drexler et al., 2014).

Thus, the optimal sample size for the study was calculated as:

𝑛∗ = 2(𝑍𝛼 +𝑍1−𝛽)2𝜎2 / (∆2) +1 (1)

; 𝑛∗ = 2(1.28 +0.8416)2 0.252 / (0.072) +1=116

Financial Factors

• Group-based loans

• Group financial training

• Financial book keeping

Managerial Factors

• Inventory

Management

• Coaching

• Merchandising

Strategic-Fit factors

• Joint supplies sourcing

• Innovation partnerships

• Technology use

Financial Performance

• Sales growth


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Table 3.1: Sample breakdown

Description Sample

Number

Retailer

Population

Treated group: Number of

informal micro enterprises

onboarded in business

groups on Smart Duka

Project as at Jun 2018

116

Control group: Informal

enterprises not in business

groups on Smart Duka

Project

116

Total 232 874

Data Collection Methods Primary and secondary data from the project was used to get

quantitative data collected from the sample of 116 retail

enterprises in groups randomly picked using probability sampling

from the project and 116 retail enterprises not in groups. The

secondary data was obtained from the Non-governmental

organization that has the records for different cohorts both at the

baseline as from December 2015 and end line in June 2018.

Primary data was directly collected from the micro retail

enterprises randomly selected by use of a questionnaire and with

the aid of research assistants. The closed type questionnaires that

were used were structured on the specific objectives of the study

using Likert scale for rating the questions hence ordinal data for

the dependent and independent variables obtained.

3.3 Data Analysis Data was analyzed using quantitative analysis methods using

ordinal regression modelling and diagnostic, descriptive and

inferential statistics obtained as the data was quantitative in nature

and hence findings were inferred for the whole population.

Statistical Package for the Social Sciences (SPSS) was used for

the analysis. Demographic summary statistics using Mann

Whitney U tests of nonparametric data for comparison of two

independent groups were done. Statistical assumptions/Diagnostic

tests were done for Test of Normality (Kolmogorov-Smirnov (KS)

and Shapiro-Wilk tests), Homogeneity, Multi collinearity (VIF),

Auto correlation (Durbin Watson), and Tests of Parallel

lines/proportional odds. Descriptive analysis was done on the 3

business factors (financial, managerial and strategic fit) and

financial performance using Mann Whitney U tests of

nonparametric data for comparison of two independent groups.

Inferential analysis was done using- Spearman Correlation

Coefficient and Ordinal Regression using the data on Treated

group- Enterprises that joined business groups and Control group-

Enterprises that did not join business groups were used and

regressed for both the treated and control combined and for the

treated only and control only.

The Hypothesis that was tested- Use of business groups has

a positive effect on financial performance of the informal micro

retail enterprises and used the difference of two

populations/samples using mean or P value

• H0- Null Hypothesis- Financial performance of

enterprises in business groups ≤ financial performance of

enterprises not in business groups

• H1- Alternative Hypothesis- Financial performance of

enterprises in business groups > financial performance of

enterprises not in business groups

To increase the efficiency of the estimates, the study

followed Sayingzoga, Bulte & Lensink (2016) who include

control variables (covariates).

The estimate followed the model equation by Drexler et al.

(2014) below which was used to analyze the impact of training on

micro enterprises in a study in Dominican Republic:

PERALL = 𝛼 + 𝛾2FINALL+𝛾3MANALL+ 𝛾4STRALL+ 𝛾1GRP + 𝛿𝑋𝑖ALL

+ε𝑖 (2)

PERTRT = 𝛼 + 𝛾5FINTRT+𝛾6MANTRT+ 𝛾7STRTRT+ 𝛿𝑋𝑖TRT +ε𝑖 (2a)

PERCON = 𝛼 + 𝛾8FINCON+𝛾9MANCON+ 𝛾10STRCON+ 𝛿𝑋𝑖CON +ε𝑖

(2b)

Where;

PERALL –Financial Performance for all (treated and control)

sample enterprises

PERTRT – Financial Performance for treated group only sample

enterprises

PERCON – Financial Performance for control group only sample

enterprises

GRP – Group Participation Dummy (where 1 was a representation

of Yes in group and 0 No not in group)

FINALL - Financial Factors: “for all-treated and control group”

(were obtained by the average aggregate from the loans, financial

training and financial bookkeeping data)

FINTRT - Financial Factors: “for treated group only” (were

obtained by the average aggregate from the loans, financial


FINCON - Financial Factors: “for control group only” (were

obtained by the average aggregate from the loans, financial


MANALL - Managerial Factors: “for all-treated and control group”

(were obtained by the average aggregate from the inventory

management, coaching and merchandizing data)

MANTRT - Managerial Factors: “for treated group only” (were

obtained by the average aggregate from the inventory


MANCON - Managerial Factors: “for control group only” (were

obtained by the average aggregate from the inventory


STRALL - Strategic Fit Factors: “for all-treated and control group”

(were obtained by the average aggregate from the joint supplies

sourcing, innovation partnerships and technology use data)

STRTRT - Strategic Fit Factors: “for treated group only” (were

obtained by the average aggregate from the joint supplies


STRCON - Strategic Fit Factors “for control group only” (were

obtained by the average aggregate from the joint supplies


𝑋𝑖 ALL - Vector of covariates: “for all-treated and control group

(included age, level of education, gender, marital status, number

of employees)


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𝑋𝑖 TRT - Vector of covariates: “for treated group only” (included

age, level of education, gender, marital status, number of

employees)

𝑋𝑖 CON - Vector of covariates: “for control group only” (included

age, level of education, gender, marital status, number of

employees)

Anderson (n.d.) recommends collecting of all

controls/covariates that are most likely to confound on the

relationship between the independent and dependent variable as a

means of taking care of endogeneity problem. In the study various

control variables were used. They conclude that in experimental

designs, especially the ones with mediation, endogeneity exists

from measurement errors, confounding variables and effects on

selection.

3.4 Reliability and Validity Reliability refers to consistency and replication and is

concerned with the robustness of the questionnaire and whether or

not it produces consistent findings under different conditions and

different timings (Saunders et al., 2009). To assess reliability, the

internal consistency approach was used which involves correlating

responses to questions in the questionnaire to each other across a

subgroup of questions. The Cronbach’s alpha was used to measure

internal consistency and the primary data obtained during the

study was used to compute reliability. There were initial plans to

have a pilot study, however, the number of the enterprises in the

smart duka project were fixed as the study reference period was

based on prior years and hence it proved difficult to get a different

set of enterprises to do the pilot test on as the same enterprises

would have been included in the final study which would have

affected the participants responses hence affecting the study

results. Internal validity refers to the ability of the questionnaire to

measure what it is intended to measure also known as

measurement validity. Internal validity occurs when the research

shows a causal relationship between two variables, from the

research results the results did show good measurement as the

results were able to show the causal relationships between the

dependent and independent variables using the correlation and

regression analysis results hence confirming internal validity.

Content validity which is the extent to which the questions provide

adequate coverage to the investigative questions was checked

based on the literature reviewed. Based on the literature reviewed

there was adequate coverage of the sub factors under each of the

factors and performance section by having 2 questions covering

each sub factor hence good content validity. External validity

which refers to study’s research findings being generalized to

other relevant groups was checked to see if statistical

generalisability can be used using the results of both the treated

and control group which were in sync across the business factor

data and the financial performance data hence indicating external

validity.

This study is based on an experiment/project using

quantitative data. The quality of the research was assessed on the

randomness that the control and treated group were created, and

the presence of a control group removing any possible effects of

an alternative explanation to the intervention and eliminate threats

to internal validity. The control group was subjected to the same

external influences as the treated group apart from the group

participation being tested.

3.5 Reliability Reliability of the study instrument was determined to

establish the level of consistency of study measurement over time.

Using Cronbach’s alpha coefficient values between 0 and 1.00,

Kothari (2012) stated that the rule of thumb for Cronbach’s alpha

is that the closer the alpha is to 1 the higher the reliability. Findings

were presented in Table 3.2.

Table 3.2: Reliability Results

Construct Measurements

Cronbach’s alpha Number of Items

Financial factors Managerial factors Strategic-fit factors Financial performance

.712

.815

.865

.923

6 6 6 6

Source: Research Findings (2020)

Findings in Table 3.2 established that the Cronbach’s alpha

for all the study variables was above .7 or closer to 1.00, hence the

research instrument was considered reliable and was used in the

study. The reliability of the constructs was deemed excellent in

establishing internal consistency, suggesting that the instrument of

the study was applicable in giving consistent results over time.

IV. FINDINGS

4.1 Financial Factors This section is aimed to assess the significance of the

differences of various financial factors between the treated and

control groups of micro enterprise under Smart Duka programme.

Tests statistics from Mann Whitney U test were used to interpret

the study findings as presented in table 4.8. Likert scale of 1-5 had

been used with 5 being most favourable.


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Table 4.8: Financial Factors Descriptive Results

N

General Mean Rank Test Statisticsa

Mean Std. Dev Control Treated z score Sig.

I am currently able to receive loans for my business pursuits I have access to more than one business loan I am able to access financial training if I need to I perceive myself to be financially literate I can readily get information on financial bookkeeping I regularly keep my financial bookkeeping records upto date Composite Mean

220 220 220 220 220 220

2.09 2.03 4.07 4.39 2.40 4.31 3.22

1.286 .849 .737 .716 1.087 .826

109.78 111.83 111.73 106.03 107.84 111.47 110.89

111.86 107.97 108.18 118.96 115.55 108.66 109.76

-.247 -.478 -.491 -1.610 -.915 -.344 -.129

.805 .632 .623 .107 .360 .731 .900


(Table 4.8) presents the findings on financial factors of the

study. The overall composite mean of 3.22 clearly illustrate that

respondents agreed that financial factors are critical for the success

of the business. This was supported by the fact that across the two

groups of the study, majority with a mean of 4.39 agreed that they

perceive themselves to be financially literate. Also, respondents

with a mean of 4.31 and 4.07 agree that they can regularly keep

their financial bookkeeping records upto date and that they are

able to access financial training if they need to respectively. The

study also presented the mean findings of the two groups of the

study using mean rank. The overall composite mean rank of

control group was 110.89 while that of treated group was 109.76.

The Control group had a higher ranking in terms of financial

factors. There was slight difference between the groups. Using test

statistics, findings revealed that across the two groups of the study,

there was insignificant difference of application of financial

factors in their businesses. Overall significance value of .900 > .05

confirms that challenges of financial accessibility remains

common for the two groups of businesses (table 4.8). Thus, despite

the difference of mean rank being higher for the control group the

difference between the two groups was statistically insignificant.

The findings of this study therefore disagrees with the study

findings of Kodongo and Kendi (2013) whose study insinuated

that enterprises in groups may apply financial factors effectively

than individual enterprises.

Further, respondents in these two groups of the study also

disagreed that they are currently able to receive loans for their

business pursuit (mean 2.09 < 3.0); as well as being unable to have

access to more than one business loan (mean 2.03 < 3.0). Based

on these findings, the study can affirm that despite efforts made by

the programme to enhance financial aspects of these micro

enterprises, financial factors especially access to various loans

remains a challenge for many business. Therefore, programmes to

educate these micro enterprises how they can have access to

capital or ease loan availability and improve their bookkeeping

would be important as suggested by Tinajero and Lopez-Acevedo

(2010).

4.2 Managerial Factors This section is aimed to assess the significance of the

differences of various managerial factors between treated and

control groups of micro enterprise under Smart Duka programme.

Tests statistics from Mann Whitney U test were used to interpret

the study findings as presented in table 4.9.

The second objective of the study was to assess the effect of

managerial factors on financial performance of micro enterprises.

The findings (table 4.9) revealed that there was a composite mean

of 3.35 > 3.0 for the study. This could imply that respondents

across the two groups of the study agree that managerial factors

are effective in enhancing performance of the business. This

finding is in agreement with the previous finding of Asah,

Olufunzo et al (2015) who found out that strategic planning, and

efficient management of enterprises resources can efficiently

enhance enterprise performance and growth. This was supported

by the findings that respondents strongly agreed that I have been

able to organize my shop in a manner appealing to my customers

through merchandising, with an overall mean of 4.15 for the two

groups. They also agreed that they have been able to get more

customers due to the merchandising managerial concept

introduced to them during the course of project period with an

overall mean of 4.73 (table 4.9). Merchandizing is a practise of

arranging and displaying goods in an orderly manner which is

appealing to the customers and helps customers identify what they

need easily and identify product information easily like pricing.

Likert scale of 1-5 had been used with 5 being most favourable.


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Table 4.9: Managerial Factors Descriptive Results

N


Mean Std. Dev Control Treated

z score Sig.

My inventory management skills are sufficiently advanced to enable effective management of records I keep an upto to date track of my inventory regularly I have access to sufficient coaching facilities I regularly receive coaching advice regarding handling of my business I have been able to organize my shop in a manner appealing to my customers through merchandising I do get more customers after organizing my shop using merchandising skills Composite Mean

220 220 220 220 220 220

2.60 4.10 2.08 2.48 4.15 4.73 3.35

1.211 .593 1.063 .914 .687 .586

109.20 110.66 107.77 106.93 111.38 110.50 107.53

112.95 110.20 115.66 117.26 108.84 110.50 116.13

-.431 -.064 -.921 -1.22 -.344 .000 -.959

.666 .949 .357 .222 .731 1.00 .337


While aspects of merchandising was considered effective for

the success of performance of micro enterprises by the

respondents, the respondents also indicated that they disagree that

they have had sufficient coaching facilities, with a mean of 2.08 <

3.0 (table 4.9). However, this low ranking aspect is due to the fact

that coaching is done intensively during the project period when

funding of the programme is ongoing hence the coaching intensity

was not felt as much after this. The issues disagreed upon by the

respondents who are business owners, have indeed been

mentioned by Solly (2016) whose study argued that managerial

issues such as coaching remain critical factors that may hinder the

performance of SMEs. That is, lack of professionalism and

efficient coaching practices for micro enterprise owners may

hinder efficient management of business resources. From the

mean rank findings, the study established control group had a

mean rank of 107.53 while treated group had a mean rank of

116.13. The Treated group had a higher ranking in terms of

managerial factors. Using test statistics to establish the difference

between the group of the study based on managerial factors,

findings indicated a significance value of .337 > .05, suggesting

that there is no significant difference in managerial factors across

the two groups of the study. Thus, despite the difference of mean

rank being higher for the treated group the difference between the

two groups was statistically insignificant.

4.3 Strategic Fit Factors This section describes the assessment of significance of the

differences of various strategic fit factors between the treated and

control groups of micro enterprise involved in the programme of

the study. Mann Whitney U test statistics were used as shown in

table 4.10.

Table 4.10: Strategic Fit Factors Descriptive Results

Obs



I have partnered with other businesses to get better deals on orders I get tips on better prices from other businesses I have engaged with other businesses to come up with innovative solutions to problems I have learnt other ways of handling business issues or business ventures from other businesses I have used technology to improve the efficiency of my business I have access to a smartphone that I can use in my business Composite Mean

220 220 220 220 220

2.11 2.85 2.69 2.25 2.26

1.023 1.210 .986 1.264 .897

106.84 106.26 107.25 108.55 107.22

117.43 118.54 116.66 114.20 106.72

-1.227 -1.428 -1.091 -.656 -1.111

.220 .153 .275 .512 .267


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220

4.22 2.73

.641 108.07 105.49

115.11 120.00

-.918 -1.615

.359 .106


The third objective of the study sought to determine the

effect of strategic fit factors (joint supply sourcing, innovation

partnership and technological use) on the financial performance of

micro enterprise businesses in the programme of the study.

Findings as presented in (table 4.10), clearly indicate that

respondents across the groups of the study did not entirely agree

(disagree) that strategic fit factors has been effective in the

performance of the informal micro enterprises (composite of 2.73

< 3.0). This is because respondents across the study groups

disagreed that they have partnered with other businesses to get

better deals (mean of 2.11 < 3.0). Despite majority of the

respondents agreeing that they have access to smartphones that

they can use in their business (mean of 4.22 > 3.0), they also

disagreed that they have used technology to improve the efficiency

of their business (mean of 2.26 < 3.0). Findings also showed that

respondents disagreed that they get tips on better prices from other

businesses (mean of 2.85 < 3.0). The study findings therefore did

not fully support Kibry and Kaiser (2003) whose study found out

that joint-ventures and group participation improves the

competencies, and allow for the common sourcing of resources for

businesses by the enterprises.

Using the mean rank findings, the study showed that the

overall mean rank for control group of the study was 105.49 while

mean rank for treated group was 120.00. The Treated group had a

higher ranking in terms of strategic fit factors. Test statistics based

on the significance value of the study indicated that all the

constructs of strategic fit factors across the groups of the study

were as insignificant. That is, they all had a significance value

above .05. Overall significance value of .106 > 0.05 suggested that

there was no significance difference in effective of usage and

benefits of strategic fit factors across the two groups of the study.

Overall, the observation was that the level of strategic fit factor is

ineffective. Also to note is that, despite treated group having some

strategic fit such as better deals, the level of effectiveness of the

factor in these businesses has been low. This is despite studies

such as Hoffman and Schlosser (2001) indicating that strategic

alliances are currently gaining popularity across businesses with

the aim of enhancing enterprise competencies. Thus, despite the

difference of mean rank being higher for the treated group the

difference between the two groups was statistically insignificant.

4.4 Financial Performance The study examined the financial performance of informal

micro enterprises of Smart Duka Programme by examining level

of agreements on the previous performance of the business. Using

performance constructs, the study used Mann Whitney U test

statistics to indicate the significance difference in performance

between the two groups of the study.

The study aimed to establish performance of the informal

micro enterprises across the study groups, as well as the difference

in performance between the groups, using Mann Whitney U test

for significant difference. First, the study established as presented

(table 4.11) the overall performance of the two groups. Composite

mean of 3.56 > 3.0 showed that respondents agreed that

performance of the businesses across the groups have been fair or

good. This was in accordance with respondents agreement that

over the past two years, they have been able to increase their

inventory (mean of 3.63 > 3.0), over the two years, I have seen my

shop increase in size (mean of 3.80 > 3.0) (table 4.10). Across all

the constructs of performance, respondents agreed that the

business have been faring well (general mean above 3.0).

The study also established mean ranks for each groups of the

study. Overall mean rank established that control group had a

mean rank of 104.95 while treated group had a mean rank of

121.01. The Treated group had a higher ranking in terms of

financial performance factors. Using test statistics of significance

value, findings established that there was a significant difference

between the two groups based on the performance of the

businesses in the last two years. That is, treated group have

performed better than control as indicted by a significance value <

.05 in 3 of the factors under financial performance on increase in

inventory, increase in profit and more access to cash float over the

last 2 years. However, the overall significance value of .074 > .05

established insignificant difference in performance of informal

micro enterprises in control and treated groups (table 4.11). Thus,

despite the difference of mean rank being higher for the treated

group the difference between the two groups was statistically

insignificant.

Table 4.11: Financial Performance Descriptive Results

N



Over the past two years I have been able to increase my inventory Over the past two years I have seen my shop increase in size Over the past two years I have seen a significant increase in sales

220 220 220 220

3.63 3.80 3.65 3.34

1.067 1.027 .969 1.142

104.95 105.33 106.34 105.90

121.01 120.29 118.38 119.22

-2.111 -1.775 -1.462 -1.532

.035 .076 .144 .125


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Over the past two years I need to replenish my stock more often as most of my stock is quickly depleted due to faster sales Over the past two years I have noted a significant increase in profit Over the past two years the business has more access to cash float Composite Mean

220 220

3.38 3.55 3.56

1.016 1.221

104.35 104.05 104.95

122.14 122.73 121.01

-2.149 -2.137 -1.788

.032 .033 .074


Inferential Statistics This section aimed at providing critical information for

making study conclusions. Four main sets of analysis were

applied. First, the study estimated the correlation analysis,

robustness or test of model significance, Kruskal wallis which is a

non-parametric one way ANOVA analysis and regression

coefficient which forms part of regression analysis. Ordinal

regression analysis was performed since the study used ordinal

data.

Correlation analysis To assess the correlation analysis, the study used Spearman’s

correlation coefficient which measures the strength of relationship

between variables in the study (financial factors, managerial

factors, strategic fit factors and financial performance). The

Spearman’s coefficient ranges between -1 and +1. A negative

coefficient value shows negative correlation whereas a positive

coefficient shows positive correlation. However, a coefficient

value of 0.5 and above was considered strong while below 0.3 was

considered weak.

The Spearman’s correlation coefficient summary shown on

table 4.12 reveals that the correlation between managerial factors

and strategic fit factors with financial performance was positive

and significant; at significant level of 0.05 and 0.01. This was

established after determining the differences between treated and

control micro enterprise groups of the study. The correlation

analysis to find out the strength of relationship between financial

factors measured by (loans, group financial trainings and financial

bookkeeping) and financial performance was insignificantly

correlated (r = .024, p > 0.05). This indicated a weak positive and

insignificant correlation between financial factors and financial

performance of group informal micro enterprises. The study also

established a positive significant correlation between managerial

factors measured by (inventory management, coaching and

merchandising) and financial performance (r = .169, p < 0.05).

Table 4.12: Spearman’s Correlation Coefficient Results

Correlations

Financial performance

Financial factor

Managerial factor

Strategic fit factor

Group

Financial performance Financial factor Managerial factor Strategic fit factor

Spearman’s Correlation Sig. (1-tailed) N Spearman’s Correlation Sig. (1-tailed) N Spearman’s Correlation Sig. (1-tailed) N Spearman’s Correlation Sig. (1-tailed) N

1 220 .024 .364 220 .169** .006 220 .194** .002 220

1 220 .381** .000 220 .224** .000 220

1 220 .368** .000 220

1 220

Group Spearman’s Correlation Sig. (1-tailed) N

.121* .037

-0.009 .450

.065 .169

.109 .053

1


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220 220 220 220 220

**Correlation is significant at the 0.01 level (1-tailed)

*Correlation is significant at the 0.05 level (1-tailed)

Source: Author (2020)

This revealed a weak but significant relationship between the

variables. Lastly, the findings established a weak but significant

positive correlation between strategic fit factors measured by

(joint suppliers sourcing, innovation partnership and technology)

and financial performance (r = .194, p < 0.05). Of the three

variables of the study, the study established that both strategic fit

and managerial factors had a significant but weak positive

correlation with financial performance. However, financial factors

had insignificant weak positive correlation as presented in table

4.12.

Further, a correlation analysis on business group (treated and

control) and financial performance of informal micro enterprises

established a weak but significant positive correlation (r= 0.121,

p<0.05) as shown in table 4.12. This finding reaffirmed the

findings of Siekei, Wagoki and Kalio (2013) whose study found

out that businesses that tend to participate in groups are more

effective than individual businesses. They gave out reasons such

as training, coaching and partnership as reasons why these

businesses get through group participation, unlike individual

micro enterprises. The findings were also echoed by Kibry and

Kaiser (2003) who indicated that joint ventures have high level of

competencies compared to individually managed ventures.

4.6.2 Regression Analysis Ordinal Regression analysis was carried out to establish the

relationship between independent variables (financial factors,

managerial factors and strategic fit factors) and dependent variable

(financial performance) of the study. The analysis was assessed to

show the effect of financial factors, managerial factors and

strategic fit factors on financial performance of micro enterprises.

The analysis was based on the equation (2) of the study as

discussed on methodology section of the study.

4.6.2.1 Model Summary

Model summary was estimated to establish percentage of

variation in the financial performance of informal micro

enterprises caused by factors in business groups such as financial

factors, managerial factors and strategic fit factors using Pseudo

R-Squares. As shown in table 4.13, Pseudo R-Square has three

squared values. For explaining the percentage change on financial

performance of informal micro enterprises caused by independent

variables, the study uses Nagelkerke R-Square value of .150. This

explains that that 15.0% of changes in financial performance of

informal micro enterprises involved in the study are caused by

changes in financial factors, managerial factors and strategic fit

factors, together with control factors. This implies that other

changes (85.0%) may be caused by other factors not included in

the study. Correlation coefficient which reveals the relationship of

study variables is explained by Cox and Snell R-Square value of

.015, which indicates that there is a positive relationship between

study variables.

Table 4.13: Model Summary

Pseudo R-Square

Cox and Snell Nagelkerke McFadden

.150

.150

.030

Link function: Logit. Source: Author (2020)

4.6.2.2 Model Fitting

Table 4.14: Model Fitting-Ordinal Regression

Model Fitting Information

Model -2 Log Likelihood

Chi-Square df Sig.

Intercept Only Final

1204.668 1168.969

35.700

13

.001

Link function: Logit.


Table 4.15: Model Fitting-Generalized linear model-ordinal

logistic

Omnibus Testa

Likelihood Ratio Chi-Square df Sig.

35.700

13

.001

a. Compares the fitted model against the

thresholds-only model


Logistic regression is based on logit which deals with the

natural logarithm of an odds ratio and are used to test hypotheses

on relationships of an outcome variable that is categorical and one

or more predictor variables that are categorical or continuous

(Peng et al., 2002). The logit is the natural log of the odds or

expressed as probability/(1-probability). The Chi-Square in (table

4.14) using ordinal regression is the Likelihood Ratio (LR) Chi-

Square test. It tests whether at least one of the independent’s

regression coefficient is not equal to zero in the model of the study

and can be established by (1204.668 – 1168.969 = 35.700).

Significance (Sig.) is the probability of getting a LR statistic above

the null hypothesis which assumes that regression coefficients of

independent variables are zero and that the intercept only/null

model is better than the final model.

The Chi-Square test, tests if there is significant improvement

in fit of final model relative to intercept only model. The study

established a significance level (p = .001 < .05) which shows that

at least one of the regression coefficients in the model is not equal

to zero, and that the model is fit and shows significant

improvement in fit of final model relative to intercept only model

at significance level of .001 < .05. The Likelihood ratio Chi-

Square from omnibus test as shown in table 4.15 using generalized

linear model- ordinal logistic gave the same results as the ordinal


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regression under Ordinal regression results on table 4.14 hence

providing legitimacy to the ordinal regression results which were

similar to the generalized linear model-ordinal logistic.

Goodness of Fit

The study performed goodness of fit to estimate whether data

for the study is consistent with the model or if data fits the model

as shown in table 4.16.

Table 4.16: Goodness of Fit

Goodness of-Fit

Chi-Square

df Sig.

Pearson Deviance

3862.238 1168.969

4523 4523

1.000 1.000

Link function: Logit

Source: Author (2020)

Table 4.16 presents Goodness of fit results. The null

hypothesis states that the model in use is adequate in comparison

to a perfect model with all cell counts and is a good model fit to

data (Peng et al., 2002). The study established a significance value

(p = 1.000 > .05) which reveals that data fits the model of the study

and the model is adequate in comparison to a perfect model.

Kruskal Wallis Test

The Kruskal Wallis test which is the nonparametric one

way ANOVA was done with results presented in table 4.17 from

the results all the factors and covariates were non significantly

different from the two groups-Treated and control as the

significance levels were all >0.05. The significance results were

similar to the significance under descriptive results in table 4.8,

4.9, 4.10 and 4.11 and demographic summary statistics in table

4.2.

Table 4.17:Kruskal Wallis Test

Kruskal Wallis-Test Statistica,b

Financial Factors

Managerial Factors

Strategic Fit Factors

Financial Performance Factors

Kruskal Wallis H df Asymp. Sig.

.016 1 .900

.920 1 .337

2.609 1 .106

3.197 1 .074

Age Gender

Marital Status

Education

No. of employees

Kruskal Wallis H df Asymp. Sig.

3.666 1 .056

1.602 1 .206

.464 1 .496

723 1 .395

638 1 .425

a. Kruskal Wallis Test

b. Grouping Varible: Treated/Control


4.6.2.5 Regression Coefficients/Estimates

To accept or reject the null hypothesis of the study,

regression estimates with a significance level of 0.05 was used to

estimate the effect and extent of effect of financial factors,

managerial factors and strategic fit factors on financial

performance. Control factors/variables were also included in the

model. Ordinal regression was estimated and findings presented in

table 4.18.

Table 4.18 presents the findings on ordinal regression

estimates which were similar to the generalized linear model-

ordinal logistic. The study established that of the three predictor

variables of the study; that is, financial factors, managerial factors

and strategic fit factors; only financial factors had a negative

estimate on financial performance (𝛽 = -.164). Both managerial

and strategic factors had positive estimates of (𝛽 = .381) and (𝛽 =

.596) respectively. Both financial factors (p = .597 > .05) and

managerial factors (.198 > .05) had insignificant effect on the

performance. However, managerial factors had insignificant

positive relationship while financial factors had insignificant

inverse relationship with performance of informal micro

enterprises in the study. Only strategic fit factor as a predictor

variable of the study had significant strong positive relationship (p

= .004 < .05) with financial performance of informal micro

enterprises in the study. For the managerial and strategic fit factors

which had positive estimates, this means with increasing scores of

the managerial and strategic fit factors there are increased chances

of falling at a higher level of the financial performance variable.

However, for the financial factors which had a negative estimate,

this means as the financial factors increase there is decreased

probability of falling at a higher level on the financial performance

variable.

Therefore, from the data extracted from table 4.18, the

regression equation was based on the equation (2) of the study.

This indicates that if all factors were held constant; financial

performance of the control and treated informal micro enterprise

groups would be at 12.348 on the higher level as presented on table

4.18.


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Table 4.18: Regression Estimates Results

Parameter Estimates

Estimates Std. Error Wald df Sig.

95% Confidence Interval Lower Bound

Upper Bound

Threshold Location

[ses = 1.333] [ses = 4.666] Financial factors Managerial factors Strategicfit factors

1.057 12.348 -.164 .381 .596

2.322 2.403 .310 .296 .209

.207 26.408 .280 1.257 8.155

1 1 1 1 1

.649

.000 .597 .198 .004

-3.494 7.638 -.772 .199 .187

5.607 17.057 .444 .960 1.005

[2] Group Treated Control

.254 0a

.256 .

.986 .

1 0

.321 .

-.755 .

.247 .

[3] Age (<25 Years) (25-35 Years) (>35 Years) Gender Male-1 Female-2 Marital-Status Married-1 Divorced-2 Single-3 Education Tertiary-1 Secondary-2 Primary-3 No. of employees 1 person 2-5 people 6-9 people

-.540 -.055 0a .012 0a .990 -1.514 0a .603 -.143 0a 3.365 3.324 0a

.805 .263 . .245 . .595 1.388 . .486 .431 . 1.852 1.943 .

.449 .043 . .002 . 2.763 1.188 . 1.537 .110 . 3.302 2.926 .

1 1 0 1 0 1 1 0 1 1 0 1 1 0

.503 .835 . .962 . .096 .276 . .215 .740 . .069 .087 .

-2.119 -.570 . -.468 . -.177 -4.235 . -.350 -.988 . -.265 -.485 .

1.039 .461 . .491 . 2.156 1.208 . 1.556 .702 . 6.995 7.133 .

(4)Location Financial factors Managerial factors Strategicfit factors

-.546 .485 .091

.514

.467 .403

1.131 1.078 .051

1 1 1

.288

.299 .821

-1.553 -0.431 .698

.460 1.400 0.880

(5)Location Financial factors Managerial factors Strategicfit factors

.199

.282 .796

.407

.404 .257

.238

.489 9.548

1 1 1

.626

.484 .002

-.600 .509 .291

.997 1.073 1.300

Link function: Logit

a. This parameter is set to zero because it is redundant

(2) Group participation regression coefficient

(3) Control factors as presented in equation (2)

(4) Treated group regression coefficients with control factors as presented in equation (2a)

(5) Control group regression coefficients with control factors as presented in equation (2b)


However, with changes such as unit increase in managerial

factors and strategic fit factors, there is a predicted increase of

.381 and .596 respectively of log odds of falling at a higher level

of the financial performance of informal micro enterprises. As for

a unit increase in financial factors, there is a predicted decrease of

.164 of log odds of falling at a higher level of the financial

performance of informal micro enterprises. This therefore implies

that only managerial and strategic fit factors variables have a

positive effect on the financial performance of informal micro

enterprises of the study, with managerial factor having


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insignificant positive effect and strategic fit factor having

significant strong positive effect on financial performance. Also,

strategic fit factor was the most dominant factor as it contributes

59.6% of changes in the financial performance of the informal

micro enterprises.

It was followed by managerial factor which contributes

38.1% on the performance of informal micro enterprises as shown

in table 4.18. These findings are in agreement with the previous

findings of Asah, Olufunso et al (2015) whose study indicated that

strategic planning through applying effective management

practices such as coaching and merchandising which entails

arrangement of products/resources of the micro enterprises can

enhance enterprise growth. Also, Hoffman and Schlosser (2001)

affirmed that businesses that form strategic alliances through

groups attain business competencies which assist in micro

enterprise management and pricing, leading growth of the

business.

Parenthesis [2] in table 4.18 indicates the estimates of group

effect. Findings shows that group factor have insignificant positive

effect on performance of informal micro enterprises (𝛽 = .254, p

= .321 > .05). Based on the findings in parenthesis [2], this means

with increasing scores of the group participation (treated group)

relative to the enterprises not in groups (control group) there are

increased chances of falling at a higher level of the financial

performance variable although these is statistically insignificant.

With unit increase in group participation, there is a predicted

increase of .254 of log odds of falling at a higher level of the

financial performance of informal micro enterprises. The study

also presented findings on control factors as shown in table 4.18

in parenthesis [3]. Of all the control variables of the study; age,

gender, education, marital status and number of employees, none

had significant effect on the performance of informal micro

enterprises. All had significance values > .05. This is similar to the

significance results on the demographic summary statistics on

table 4.2.

The study also presented findings on treated group data

based on equation (2a) as shown in table 4.18 in parenthesis [4].

Of all the factors -financial , managerial and strategic fit factors

were statistically insignificant, all had significance values > .05.

The managerial and strategic fit factors had positive estimates

while the financial factors had negative estimate which would

increase and decrease respectively the probability of falling at a

higher level of the financial performance. It was only the

managerial factor estimate that was higher under treated group in

comparison to control group

The study also presented findings on control group data

based on equation (2b) as shown in table 4.18 in parenthesis [5].

Of all the factors -financial and managerial factors were

statistically insignificant, had significance values > .05 while

strategic fit factors were statistically significant, had significance

values <0.05. All factors, financial, managerial and strategic fit

factors had positive estimates which would increase the

probability of falling at a higher level of the financial performance.

The financial and strategic fit factors had higher estimates in the

control group in comparison to the treated group while the

strategic fit factor was significant in the control group while in the

treated group it was not.

V. DISCUSSION, CONCLUSIONS AND

RECOMMENDATIONS

5.1 Discussion of the Findings This section discusses findings of the study based on the

research objectives. The study was guided by three specific

objectives; to assess the effect of financial factors, managerial

factors and strategic fit factors in business groups on financial

performance of informal micro retail enterprises in the Smart

Duka Project. The analysis of the study was based on business

groups. Micro enterprises in the group were referred to as treated

group while those not in groups were referred to as control group.

Difference in difference analysis using Mann Whitney U test

statistics was conducted at the descriptive level to determine how

each factor performed across the business groups. Discussion of

the study follows:

Effect of business group financial factors on financial

performance The first objective of the study was to assess the effect of

financial factors in business groups on financial performance of

informal micro retail enterprises in the Smart Duka project.

Financial factors were measured by group based loans, group

financial trainings and financial bookkeeping. From the

descriptive findings, the results revealed that financial factors

remain a major challenge for most businesses, both treated and

control groups. Majority of the respondents indicated that

accessibility to loan and frequent financial trainings on how to

record sales have not been effectively mastered by the business

owners. Despite having been coached on how to undertake

bookkeeping practices so as to be able to track sales and financial

performances, the findings revealed that most micro enterprises do

not follow these practices. The mean ranking of the control group

was above that of the treated group under the financial factors

however, this difference was not statistically significant.

The study also established a correlation between financial

factors and financial performance of the informal micro retail

enterprises. The findings established a weak insignificant positive

correlation between the variables of the study. One important

assumption made during correlation analysis was based on the

descriptive findings. This is because from the respondents’

perspectives, micro enterprises still face financial factors

challenges, both treated and control group. Only a few respondents

confirmed that they could access loans and trainings. However,

majority of the respondents indicated that most of their businesses

are not doing well as a result of lack of access to enough funds to

increase their inventories in the business. This finding agrees with

previous studies (Tinajero & Lopez-Acevedo, 2010), which have

highlighted financial factors as a major challenge informal micro

enterprises faces in the current business environment.

The study also determined the effect of financial factors on

financial performance of micro enterprises using ordinal

regression estimates analysis. The results shows that there exists

insignificant negative relationship between financial factors in

group business and financial performance. This was based on the

p-value which is above the recommended significance level.

Interestingly, the study also found out that inability to access

capital, ineffective to turn financial skills in the business to

achieve results can hinder business performance. In addition to


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having weak insignificant positive correlation, the study findings

indicate that due to the aforementioned challenges that informal

group micro enterprises face, most of these businesses cannot

grow or expand. The findings concur with the findings of

Kodongo and Kendi (2013) that even though group participation

enhances business competencies and access to lending benefits,

they remain challenges that must be addressed effectively in order

to grow micro enterprises. Thus, there is insignificant negative

effect of financial factors on financial performance. On the ordinal

regression estimate results for the treated group of the businesses

in groups the financial factors had a negative estimate that was

insignificant while the control group of the businesses not in

groups had a positive estimate that was insignificant. This does

seem to tally to the findings on the descriptive results on mean

rankings where the mean ranking of the control group was higher

than the treated group although insignificant. Hence it seems for

the financial factors, these seem to work better for the control

group than the treated group and hence there is need to investigate

more why this would be so. Although the current results show the

difference is insignificant it does show that this could be a

significant difference with time and with increased financial

factors.

In summary, findings established that financial factors in

business groups remain a major challenge that must be addressed

effectively. With a weak positive correlation and insignificant

inverse effect on the financial performance of informal micro

enterprises in the programme, most respondents attributed these

challenges to inability to access loans even within business groups

as well as the inability to effectively utilize bookkeeping practices

to grow their businesses. Across the business regions and/or areas

of business, findings established that financial factors was a

challenge across, both for treated and control business groups.

Thus, there was insignificant spill over effects in relation to being

in a group that can enhance chances of getting access to loans and

other financial trainings. However, the composite mean in the

descriptive results did clearly illustrate that respondents agreed

that financial factors are critical for the success of the business but

the level of administration seems to be causing the negative effect

on financial performance from the loans, to financial bookkeeping

and to financial training practices.

Effect of business group managerial factors on

financial performance The study also sought to assess the effect of managerial

factors in business groups on financial performance of informal

micro enterprises in the Smart Duka project. From the descriptive

findings, the findings showed that respondents in both groups of

the study unanimously agreed that managerial factors measured

through inventory management, coaching and merchandising has

been effective towards the performance of their businesses. Of

importance to them was the merchandising concept which was

introduced in the programme. Majority of the respondents agreed

that they have been able to organize their shop in a manner

appealing to the consumers/customers through merchandising.

They also agreed that they do get more customers after organizing

their shop using the skills they attained through merchandising

practice training. The findings are in line with the study of Asah,

Olufunso et al (2015) that organizing an enterprise in a way that

appeals to the consumers is one of the strategic planning skills that

businesses can learn through management practices to enhance

their growth. There was no statistical significance difference in

terms of managerial factors.

A correlation analysis was determined in the study between

managerial factors in businesses and financial performance. The

findings established that there a significant correlation between

these two variables of the study. While the previous studies such

as Solly (2016) indicated that managerial issues remain critical to

the success of informal micro retail enterprises, the findings of the

current study found that to these respondents especially those in

business groups, the coaching they have received from various

stakeholders in the business programme have significantly helped

them to arrange their shops and manage their inventories in ways

that appeal to the customers in the market. Also to note is of the

three variables of the study, managerial factor had the second

highest correlation coefficient value with financial performance of

the business groups. Therefore, the study argue that across the

business groups, that is, treated and control, respondents agreed

that managerial factors has been effective towards their

businesses. The mean ranking of the treated group was above that

of the control group under the managerial factors however, this

difference was not statistically significant.

The study further found out an insignificant positive

relationship between managerial factors and financial

performance of informal micro retail enterprises under Smart

Duka project. The established higher p-value (significance level)

above the recommended level further revealed that managerial

factor have insignificant positive effect on the performance of

informal micro enterprises group businesses. Of the three

variables of the study, findings reveal that managerial factors had

the second highest positive regression estimate. This implies that

increase in coaching, merchandising practices and inventory

management increases the performance of the group businesses

insignificantly compared to variable like financial factor. The

finding agree with Atristain-Su (2018) that if business have

professionalism, planning, decision making and communication

skills, chances of growing are high. However, lack of all these can

lead to closure or poor performing businesses. Thus, there is

insignificant positive effect of managerial factors on financial

performance. On the ordinal regression estimate results for the

treated group of the businesses in groups, the managerial factors

had a positive estimate that was insignificant but higher than the

combined and control only group, however, in all instances they

were insignificant. This does seem to tally to the findings on the

descriptive results on mean rankings where the mean ranking of

the treated group was higher than the control group although

insignificant. Hence it seems for the managerial factors, these

seem to work better for the treated group than the control group

and hence there is need to investigate more why this would be so.

Although the current results show the difference is insignificant it

does show that this could be a significant difference with time and

with increased managerial factors.

In establishing the Spillover effect of business trainings that

treated group can pass to other micro enterprises, the results of the

study established that for managerial factors which comprise of

coaching, merchandising and other best management practices to

some extent, there is insignificant positive spillovers effect. One


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assumption that the study made based on the findings of spillovers

effect is that across the two groups of testing, merchandising was

introduced to each of them. Meaning all the two groups undergo

training, coaching and merchandising on similar occasion. This

could be the reason as to why the spillovers effect is positively

insignificant.

Effect of business group strategic fit factors on

financial performance The third and final objective of the study was to investigate

the effect of strategic fit factors in business groups on financial

performance of informal micro retail enterprises in the Smart

Duka programme. Strategic fit factors were measured using joint

suppliers sourcing, innovation partnerships and technology use.

From the descriptive findings, both the tested groups; treated and

control, agreed that to some extent they have been involved in joint

supply sourcing business practice so as to easily access product at

the lowest cost in the business. This supports the findings by Kibry

and Kaiser (2003) who concluded that joint ventures, among other

business competencies, allow for the common sourcing of raw

materials and other resources for the micro enterprises.

Respondents also agreed that indeed they have access to a smart

phone that can be used in their businesses. However, the study also

found out that most micro enterprises lack technological devices

that they can use to track inventory management and sales in their

businesses. This was supported by the insignificance values

established between the two groups. The mean ranking of the

treated group was above that of the control group under the

strategic fit factors however, this difference was not statistically

significant. From the descriptive findings, the findings showed

that respondents in both groups using the composite mean ranked

the strategic fit factors lowest in comparison to financial and

managerial factors

Level of innovation partnerships however are still low across

the businesses. The study established that most shop owners’

objective in joint venturing or business groups is to get tips on

better prices of the business, with little engagement with other

businesses to come up with innovative solutions to their problems.

A correlation analysis established that there is a statistical

significant positive correlation between strategic fit factors and

financial performance of micro enterprises business groups.

Among the three variables of the study, strategic fit factor had the

highest correlation coefficient value with performance of business

groups under the programme of the study. This could be due to the

ability of the business groups to bring informal micro enterprises

owners together (Hoffman & Schlosser, 2001), and allow them to

identify business challenges and seek for better management

practices that may impact their individual living standards as well

as the growth of businesses within a given business region.

The study also estimated the ordinal regression estimate

analysis between strategic fit factors and financial performance of

business groups. Findings revealed that there was a significant

strong positive relationship between these two variables of the

study. This implies that strategic fit factor in the business group

significantly affects their financial performances. Of the three

variables of the study, strategic fit factors also had the highest

positive effect on financial performance. However, analysing the

treated group alone, the strategic fit factors were insignificant

while for the control group and combined group these were

significant. Hence there is need to investigate more on this cause

of disparity. Thus, one of the assumption of the study findings is

that when businesses form groups, they can easily access better

pricing strategy and the business strategy that can allow them to

grow their businesses during and after hard economic conditions.

This is in accordance with the findings of Mungai and Ogot (2017)

whose study concluded that micro enterprises need to use diverse

competitive business strategy through value chain approaches,

technology and networks so as to improve their performance. The

spillover effect analysis in regards to strategic fit factors had a

higher estimate in comparison showing the effect of spillover that

is significant has a higher positive effect on financial performance.

5.2 Conclusions The study made a number of conclusions and was discussed

based on the research objectives of the study. The conclusion was

drawn from the research discussion and findings of the study as

discussed;

Effect of business group financial factors on financial

performance Based on the findings and discussion of the study, the study

concluded that financial factors which entail group based loans,

group financial training and financial bookkeeping remain a

challenge for many micro enterprise businesses both in treated and

control groups. Despite having an insignificant weak positive

relationship with financial performance using the correlation

results, the study also concluded that majority of the respondents

agreed that they are still unable to receive loans, as majority do not

have access to more than one loan. Most micro enterprises owners

do not practice bookkeeping practices thus establishing monthly,

quarterly, semi-annually and yearly sales is a challenge for them.

None of the respondents in both treated groups were able to

provide numerical sales volume of their businesses. From the

regression analysis financial factors had insignificant negative

effect on the financial performance of the businesses despite

having a weak positive insignificant relationship from the

correlation results. The control group had higher ranking mean

compared to the treated group which were the enterprises in

business groups under financial factors although insignificant. The

spillover effect of financial factors was also found to be

insignificant. The study therefore concluded that there is

insignificant negative spillover effect of financial factors on

financial performance of the informal micro businesses in the

Smart Duka Project. Interventions are required to address the

challenges since there is negative relationship between financial

factors and performance of informal micro enterprises involved in

the programme.

Effect of business group managerial factors on

financial performance The study concluded that of all the efforts that the

stakeholders as well as Smart Duka programme organizers have

made towards improving informal micro enterprises in the densely

populated inormal areas of Nairobi, managerial factors through

coaching, inventory management and merchandising have been

the most appreciated businesses practice by the micro enterprise


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owners. This is based on the descriptive findings of the study

where respondents across the business groups agreed that indeed,

the managerial practices has been effective, this factor had the

highest composite mean. Correlation established significant

positive relationship while regression analysis established

insignificant positive relationship between managerial factor and

financial performance of the informal micro enterprises involved

in the programme of the study. Based on this finding, the study

concludes that managerial factor have insignificant positive effect

on performance of the businesses. The treated group which were

the enterprises that were in business groups had higher ranking

mean in comparison to control group under managerial factors

although statistically insignificant. The spillover effect of

managerial factors was also found to be insignificant. The study

therefore concluded that there is insignificant positive spillover

effect of managerial factor on financial performance of informal

micro businesses in the Smart Duka Project. The differences

between the treated and control groups although currently

insignificant can be translated into significant differences with

time using various interventions given the positive effect of

managerial factors on financial performance.

Effect of business group strategic fit factor on financial

performance Based on the study finding and discussion of the study, the

study concluded that strategic fit factors have significant positive

effect on the performance of informal micro enterprises in the

Smart Duka programme. Further, the study concluded that

respondents indeed agreed that they have partnered with other

businesses to get better deals on orders and tips on better prices

from other businesses. Despite having access to smart phones, the

study concluded that majority of the informal micro enterprises

still face technology challenges. Most of the micro enterprises

both treated and control, do not have technological devices that

they can use to track inventory flow/management and daily or

monthly sales in the business. Thus, there is need for the

programme to expound on the need for group informal micro

enterprises to adopt modern technology in their businesses as they

enhance transparency, provide effective management practices

and enhance capacity of shop owners to track down their sales

easily and effectively.

Based on findings, the study concludes that strategic fit

factors have significant positive effect on performance of the

businesses. The treated group which were the enterprises in

business groups had higher ranking mean in comparison to control

group under strategic fit factors although statistically insignificant.

The spillover effect of managerial factors was also found to be

significant. The study therefore concluded that there is significant

positive spillover effect of strategic fits factor on financial

performance of informal micro businesses in the Smart Duka

Project. The differences between the treated and control groups

although currently insignificant can be translated into significant

differences with time using various interventions given the

positive effect of strategic fit factors on financial performance.

5.3 Recommendations

The findings of this study suggest that business group factors

such as managerial factors, strategic fit factors and financial

factors can indeed enhance performance of informal micro retail

enterprises involved in the study. However, the most significant of

the group the effect of these practices depend on how best micro

enterprises apply them towards improving their performances. The

study recommends that government need to formulate regulatory

and policy structures that can be useful in providing insights into

the effect of business group participation of the informal micro

enterprises sector. Policies on various business practices

especially those related to managerial and strategic fit factors

should be formulated as they have positive effects on financial

performance of the informal micro enterprises. The study also

recommends that practitioners need to design and formulate

effective structuring avenues that formal sectors in the economy

can find applicable in supporting the group businesses in the

informal micro enterprises in the economy.

REFERENCES

[1] Abor, J., & Quartey, P. (2010). Issues in SME Development in Ghana and South Africa. 39, 11.

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AUTHORS

First Author – Caroline Grace Wanjiku,

[email protected]; Caroline is a professional with over

20 years of solid results and driven experience in finance, banking,

operations, accounting, food science, post-harvest technology,

information systems and development. She is a graduate of

Strathmore Business School and her research interests are in the

area of finance and development.

Second Author – Patricia Chepwogen Chepkwony,

[email protected], Patricia is a scholar, researcher and

mentor in the field of Entrepreneurship. Patricia is a strong player

in service to humanity and employs her knowledge, skills and

experience in mentoring young people as well as women

entrepreneurs. Patricia holds a Doctorate degree in

Entrepreneurship, Master of Business administration and

Bachelors degree in Business management.


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